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result sets. Again, this additional processing will severely hamper performance. It will require that each report be constructed by a skilled developer and thus eliminate any chance that a business intelligence tool could generate a drill-across report. Furthermore, it will not work in situations where one table omits a particular product, or where a product has multiple rows in one of the dimension tables. None of these considerations takes into account the confusion that users may experience when trying to interpret the results. How does one compare orders and returns for a product if each star specifies the product differently Which product name should be placed on the report What if this report is compared to one that uses the other name The last two incompatibilities on the list may not directly hamper a drill-across operation but can lead to situations where analysts working with data sets from the two stars produce erroneous results by linking a dimension table to a fact table from the other star.
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In the case of orders versus returns, the problems from Figure 5-2 can be remedied by requiring product dimension tables that are identical in structure and content. Dimension tables can also offer a degree of compatibility when they are not identical. This occurs when the dimension tables describe things at different levels of detail. Suppose that the same company tracking orders also has sales goals, which are recorded in a sales plan. The sales goals are specified by month and sales territory, and there are various versions of the plan. Stars for orders and goals are depicted in Figure 5-3. Unlike the orders and returns, these fact tables have different grains and share no common dimension tables. Despite these differences, these stars do share some common dimension attributes. These common dimensions are highlighted in Figure 5-3. For example, each goal has an associated month, as recorded in the month table. Each order also has an associated month, as recorded in the day table. It should be possible to aggregate facts from both stars by month and then compare the results. In fact, every dimension attribute in the month table is present in the day table. Any of these common dimensions can be used to aggregate orders and goals (drill-across Phase 1) and then join the result sets together (drill-across Phase 2). Similarly, all the dimension attributes in the territory table are also present in the salesrep table. Any of these common dimensions can be used as the basis for drilling across. Figure 5-4 provides an example. The report at the bottom of Figure 5-4 compares orders and goals by month and territory. It is assembled via the same two-phase drill-across process with which you should now be familiar. In Phase 1, separate queries extract facts from each star, aggregating them to a common level of dimensional detail: month and territory. In Phase 2, these intermediate results sets are merged, and the ratio of the two facts is constructed. This process works, even though the two stars do not share a single dimension table.
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day_key full_date hoilday_flag month month_number fiscal_period year ...
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month_key month month_number fiscal_period year ...
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ORDER_FACTS
customer_key day_key product_key salesperson_key quantity_ordered order_dollars order_id order_line_num
SALES_GOAL_ FACTS
month_key version_key territory_key goal_dollars
PLAN_VERSION
version_key version_code description
PRODUCT
SALESREP
salesperson_key salesperson salesperson_id territory territory_code territory_manager region region_code region_vp ...
TERRITORY
territory_key territory territory_code territory_manager region region_code region_vp ...
Figure 5-3 These stars do not share common dimension tables but do share common dimension attributes
5 Conformed Dimensions 93
CUSTOMER ORDER_FACTS order_dollars ... PRODUCT
DAY ... month ... SALESREP ... territory ...
MONTH ... month ... TERRITORY ... territory ... Conformed Dimensions
SALES_GOAL_ FACTS goal_dollars ...
PLAN_VERSION
Orders Query ORDER DOLLARS 125,000 200,000 200,000 150,000
Goal Query GOAL DOLLARS 150,000 150,000 150,000 150,000
MONTH January January February February
TERRITORY East West East West
MONTH January January February February Phase II Merge, Compute Ratio
TERRITORY East West East West
MONTH January January February February
TERRITORY East West East West
ORDER DOLLARS 125,000 200,000 200,000 150,000
GOAL DOLLARS 150,000 150,000 150,000 150,000
PERCENT GOAL 83% 133% 133% 100%
Figure 5-4
Drilling across order_facts and sales_goal_facts
Conformed Dimensions
When dimension tables exhibit the compatibility necessary to support drilling across, they are conformed dimensions. Identical dimensions ensure conformance, but conformance can take several other forms as well. Fact tables and conformed dimensions can be planned and documented in a matrix format and serve as the blueprint for incremental implementation.
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